odML

odML (open metadata Markup Language) is a file format for storing arbitrary metadata. The underlying data model offers a way to store metadata in a structured human- and machine-readable way. Well organized metadata management is a key component to guarantee reproducibility of experiments and to track provenance of performed analyses.

python-odml is the python library for reading and writing odml metadata files. It is a registered research resource with the RRID:SCR_001376 .

The odML eco system

APIs

• odml python library. Python library for reading and writing odml files.
• java-odml-lib Java implementation of the data model.
• matlab-odml Matlab interface for odml files.

Viewer/Editor

• odml-ui: Graphical editor
• odmlTables Spreadsheet interface (by INM-6 FZ JÛlich) for odml files.

Terminologies

odML facilitates and encourages standardization by providing terminologies. An odml-file can be based on such a terminology. In that case one does not need to provide definitions since they are part of the linked terminology.

Existing terminologies can be browsed and imported from terminologies.g-node.org.

Templates

Templates are odML documents that can be re-used when collecting the same kind of information over the course of multiple identical experiments.

Templates that were created using the odML terminologies and are used to systematically collect metadata during scientific experiments can be found, discussed and shared at the templates github repository.

If you have created your own templates that others might find useful, you are encouraged to share your templates via this repository.

Currently available odML templates can be browsed and imported from templates.g-node.org.

Getting started

Installation

python-odml is most conveniently installed via pip.

pip install odml

Tutorial and examples

• We have assembled a set of tutorials.

Python convenience scripts

The Python installation features multiple convenience commandline scripts.

• odmlconvert: Converts odML files of previous file versions into the current one.
• odmltordf: Converts odML files to the supported RDF version of odML.
• odmlview: Render and browse local XML odML files without stylesheet in the webbrowser.

All scripts provide detailed usage descriptions by adding the help flag to the command.

odmlconvert -h
odmltordf -h
odmlview -h

Browsing odML files

odML XML files can be saved with an included XSL stylesheet for convenient local viewing via a web browser. To use this option, save odML documents using the XMLWriter instead of odml.save():

XMLWriter(doc).write_file("filename.xml", local_style=True)

odML - NIX conversion script

You can also find a command line script for importing metadata from odML files into the second, more data centric, data format, NIX, or exporting metadata from a NIX file to a new odML file.

You can install the command line script via pip:

pip install nixodmlconverter

The script can then be run from the command line and provides a detailed usage description by adding the help flag to the command.

nixodmlconverter -h

The source code of the converter can be found on github.

Support

If you experience problems using odml feel free to join our IRC channel #gnode at FreeNode or write an email to [email protected]. If you find a bug please report it using the project issue tracker.

License

This project is open source published under an MIT license-like see license file for details.

Citing

If you use odml, it would be much appreciated if you would cite it in publications with its identifier RRID:SCR_001376 and/or the reference:

Grewe J., Wachtler T., and Benda J. (2011). A bottom-up approach to data annotation in neurophysiology. Front. Neuroinform. 5:16, doi:10.3389/fninf.2011.00016

Referenced By

• Sprenger et al (2019) doi:10.3389/fninf.2019.00062
• Dragly et al (2018) doi:10.3389/fninf.2018.00016
• Brochier et al (2018) doi:10.1038/sdata.2018.55
• Moucek et al (2017) doi:10.1038/sdata.2016.121
• Papez et al (2017) doi:10.3389/fninf.2017.00024
• Bigdely-Shamlo et al (2016) doi:10.3389/fninf.2016.00007
• Rübel et al (2016) doi:10.3389/fninf.2016.00048
• Wiener et al (2016) doi:10.1016/j.neuron.2016.10.037
• Zehl et al (2016) doi:10.3389/fninf.2016.00026
• Jayapandian et al (2015) doi:10.3389/fninf.2015.00004
• Jezek et al (2015) doi:10.3389/fninf.2015.00003
• Kocaturk et al (2015) doi:10.3389/fnbot.2015.00008
• Maccione et al (2015) doi:10.1016/j.brainresbull.2015.07.008
• Vanek et al (2015) doi:10.1109/Informatics.2015.7377849
• Garcia et al (2014) doi:10.3389/fninf.2014.00010
• Moucek et al (2014) doi:10.3389/fninf.2014.00020
• Sobolev et al (2014) doi:10.3389/fninf.2014.00015
• Cockfield et al (2013) doi:10.3389/fninf.2013.00020
• Papez et al (2013) doi:10.1109/BIBM.2013.6732554
• Bakker et al (2012) doi:10.3389/fninf.2012.00030